Ipums

Ruggles, Steven; Sobek, Matthew

doi:10.1007/978-3-030-22009-9_980

Cited by 316 publications

(381 citation statements)

References 5 publications

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“…These databases are the 1980, 1990 Use Microdata Samples (PUMS) files of the Census and the 2005, 2010, 2015 and 2018 samples of the American Community Survey (ACS). All files were provided by the Integrated Public Use Microdata Series (Ruggles et al, 2020). The PUMS were routinely employed as they were collected through the long-form questionnaire administered to 1 in 6 U.S. households by the Census, providing detailed demographic data on over 8 million households until 2000.…”

Section: Data and Samplementioning

confidence: 99%

“…Family structure refers to the organization of marital status of parents and their relationships to children to distinguish between married-couple and single-parent families. We employ the 5% Public Use Microdata Samples of the Census (1980Census ( -2000 and the American Community Survey (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) (Ruggles et al, 2020). The first analytical step was to trace the prevalence of multiracial families amongst the larger population of families with resident minor children across time.…”

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confidence: 99%

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Counting Families, Counting Race: Assessing Visible Family Structural Change among Multiracial Families, 1980–2018

et al. 2022

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Racially mixed children are a rapidly expanding segment of American families, signaling the ongoing blurring of racial boundaries. Most of what is known about multiraciality is drawn from analyses of two-parent families even as marriage became decreasingly tied to childbearing. The current study tracked the prevalence and racial composition of multiracial families where parents are married and unmarried from 1980 until 2018 using data from the U.S. Census and American Community Survey. We find that multiracial families are increasingly common amongst married and unmarried parents with the greatest growth occurring among White unmarried mothers, nearly 15% of whom have multiracial children as of 2018. Additionally, we find that Asian-White and Hispanic-White children are more likely to live in married families while Black-White and dual minority children are disproportionately represented amongst single-parent families. Ultimately, capturing the complexity of racialized contexts where multiracial children are found, as well as how the prevalence of these contexts has changed over time, requires accounting for family structure differences. Highlights •Multiracial families with children are rapidly growing, but this is largely tracked solely among married households.• Using 1980-2018 U.S. Census and American Community Survey data, we find growing married and unmarried multiracial families.• Unmarried families are increasingly multiracial, most often composed of Black-White children with White mothers.• Meanwhile, Hispanic-White and Asian-White children are more common among married families. •The lives of multiracial children will be increasingly shaped by different racialized and gendered family contexts.

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Section: Data and Samplementioning

confidence: 99%

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confidence: 99%

Counting Families, Counting Race: Assessing Visible Family Structural Change among Multiracial Families, 1980–2018

et al. 2022

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“…Data. The data considered in the contest is a subset of IPUMS American Community Survey data [2] about demographic and financial features for some states across some years. The data about states Ohio and Illinois from 2012-2018 is given as a public dataset.…”

Section: Applicationmentioning

confidence: 99%

“…Personal information collected as flat tabular data is used in many real world applications, such as medical records, community survey records and web visit records [1,2,3]. Releasing these sensitive records to the public enables a variety of scientific research, but introduces risk due to privacy leakage.…”

Section: Introductionmentioning

confidence: 99%

Prior-Aware Distribution Estimation for Differential Privacy

Tao¹,

Bater²,

Machanavajjhala³

2021

Preprint

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Joint distribution estimation of a dataset under differential privacy is a fundamental problem for many privacyfocused applications, such as query answering, machine learning tasks and synthetic data generation. In this work, we examine the joint distribution estimation problem given two data points: 1) differentially private answers of a workload computed over private data and 2) a prior empirical distribution from a public dataset. Our goal is to find a new distribution such that estimating the workload using this distribution is as accurate as the differentially private answer, and the relative entropy, or KL divergence, of this distribution is minimized with respect to the prior distribution. We propose an approach based on iterative optimization for solving this problem. An application of our solution won second place in the NIST 2020 Differential Privacy Temporal Map Challenge, Sprint 2.

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“…Appendix C lists every case for which we update Leip's tallies. For data on state demographics including race and education, we use IPUMS extracts from decennial Censuses (Ruggles et al, 2018) and the American Community Survey. Further data details are documented in Appendix C.…”

Section: Datamentioning

confidence: 99%